1. Field of the Disclosure
The disclosure refers to a method for determining the position of moving objects such as persons or robots, for example. The method of the present disclosure is particularly suited for implementation in buildings or in regions or areas blocked to satellite navigation.
2. Discussion of the Background Art
The localization of objects, such as persons or goods, is often performed using satellite navigation, e.g. by GPS. Outside of buildings, this yields an acceptable level of accuracy even with pedestrians having modern devices. Inside of buildings or when an object is in a blind area of the satellite, such as in narrow street canyons, strong interferences often occur that are due to the blocking of the direct signal path or due to multipath errors.
In order to improve the localization of objects also in such areas, it is known to use further radio systems such as WLAN mobile phone networks, UWB (Ultra-Wide Band) networks and the like. Substantial drawbacks of a combination with other radio systems are a possibly limited availability, the necessary infrastructure and a possibly restricted access. A prerequisite for an implementation of these methods is the existence of radio infrastructures. Further, the respective areas have to be mapped and measured. This represents a substantial economic effort.
Another possibility for improving the localization of objects in buildings and the like is the use of sensors connected with the moving object and transmitting information about the movement of the object to a corresponding computing means. The corresponding sensors may be passive or active optical sensors and sensor systems. For example, these may also be inertial sensors, odometry systems in the case of robots, or barometric altimeters. The advantage is that infrastructure elements, such as described above (WLAN, UWB etc.) can be dispensed with completely or partly.
Another possibility for improving the accuracy of localization is the combination with environmental data such as building maps, for example. B. Krach, P. Robertson, “Integration of Foot-Mounted Inertial Sensors into a Bayesian Location Estimation Framework”, Proc. 5th Workshop on Positioning, Navigation and Communication 2008 (WPNC 2008, Hannover, Germany, March 2008, describes that previous knowledge about building plans and the use of an inertial sensor provided in the shoe of a person are suited for an unambiguous localization of a person in a building. By means of the inertial sensor (IMU) used, all three spatial axes can be measured. In this method, an IMU sensor is integrated in a shoe of a person moving in a building. The sensor transmits acceleration and rotation rate data to a computing means. The computing means comprises a filter means which is an Extended Kalman Filter (EKF). The filter means is used to estimate the relative change of the orientation and position of the shoe and thus of the person (so-called odometry). Orientation is understood to be the orientation in space, i.e. an indication including three angles. Position refers to the location in space (typically in a local or global 3D coordinate system). In particular when a person is in a building for a longer period of time, the effect of sensor errors (drift) on the estimation of position or orientation may possibly grow infinitely. Therefore, it is known to perform a so-called “Zero Velocity Update” (ZUPT) Here, in a rest phase of the sensor or the person, in which the shoe is on the ground, the EKF is set to zero velocity. The rest phase of the sensor or the person can be determined in a relatively reliable and simple manner, since the steps of a human show a characteristic pattern so that a kind of signature of the acceleration and rotation rates can be determined with respect to a person.
It is an essential drawback of this method, however, that due to the drift, ever increasing errors in the orientation about the vertical axis occur which can be observed only in a limited manner by means of ZUPT. As a consequence, primarily the estimation of the orientation of the person (i.e. the orientation about the vertical axis) becomes increasingly inaccurate. It is another drawback of this method that also the estimated covered distance becomes inaccurate, though to a lesser degree. Further, if this system is used exclusively, there is a drawback that only the relative positioning, especially with respect to a starting point, can be determined.
An improvement of the method using EKF and ZUPT can be achieved by linking it to a further filter means that considers environmental data such as building plans. Here, the estimation made by means of the EKF includes assumed statistical deviations relative to the step direction and the stride. The hypotheses calculated from this thus take into account all possible deviations from the actual sequence of steps of a person. By this link to environmental data, which e.g. include the walls in a building, a probability is taken into account using a particle filter algorithm. Thus, hypotheses made in the particle filter which pass through walls are either eliminated completely or are accorded a very low probability. Hypotheses that do not pass through walls are either accorded a probability value 1 or may be weighted according to a simple model of movement.
However, taking environmental data into account in this manner may lead to erroneous judgments in the determination of a position. One may for instance consider a case in which the starting position is not known exactly, and can be located inside or outside a building, for example. Based on this starting point, hypotheses that lie outside the building will be taken into account with high probability. As a consequence, even if the person is actually inside the building, hypotheses lying inside the building will increasingly be accorded a very low probability or will possibly be deleted entirely, due to the fact that they meet walls. This is not the case for hypotheses lying outside the building, since they are not accorded a low probability for meeting walls.
It is an object to provide a method for determining the position of moving objects that allows for better localization in particular in buildings and in areas blocked to satellites.